A model for the clustered distribution of SNPs in the human genome

A chaotic viewpoint-based approach to solve haplotype assembly using hypergraph model

Decreasing the cost of high-throughput DNA sequencing technologies, provides a huge amount of data that enables researchers to determine haplotypes for diploid and polyploid organisms. Although various methods have been developed to reconstruct haplotypes in diploid form, their accuracy is still a challenging task. Also, most of the current methods cannot be applied to polyploid form. In this paper, an iterative method is proposed, which employs hypergraph to reconstruct haplotype. The proposed method by utilizing chaotic viewpoint can enhance the obtained haplotypes. For this purpose, a haplotype set was randomly generated as an initial estimate, and its consistency with the input fragments was described by constructing a weighted hypergraph. Partitioning the hypergraph specifies those positions in the haplotype set that need to be corrected. This procedure is repeated until no further improvement could be achieved. Each element of the finalized haplotype set is mapped to a line by chaos game representation, and a coordinate series is defined based on the position of mapped points. Then, some positions with low qualities can be assessed by applying a local projection. Experimental results on both simulated and real datasets demonstrate that this method outperforms most other approaches, and is promising to perform the haplotype assembly.

The fractal dimension as a measure for characterizing genetic variation of the human genome

Motivated by the characteristics of highly clustered single nucleotide polymorphism (SNP) across the human genome, we propose a set of chromosome-wise fractal dimensions as a measure for identifying an individual for human polymorphism. The fractal dimension quantifies the degree of clustered distribution of SNPs and represents parsimoniously the genetic variation in a chromosome. In this sense, the proposed scheme projects the SNP genotype data into a new space which is simpler and lower in dimension. As an illustrative example, we estimate the chromosome-wise fractal dimensions of SNPs that are extracted from the HapMap of Phase III data set. To determine the validity of the proposed measure, we apply principal component analysis (PCA) to the set of estimated fractal dimensions and demonstrate that the set more or less described the population structure of 11 global populations. We also use multidimensional scaling to relate the genetic distances based on PCA to the geographical distances between global populations. This shows that, similar to the SNP genotype data, the fractal dimensions also has a role in genetic distance in the population structure. In addition, we apply the proposed measure to a signature for the classification of global populations by developing a support vector machine model. The selected feature model predicts the global population with a balanced accuracy of about 77%. These results support that the fractal dimension is an efficient way to describe the genetic variation of global populations.

Application of Chaotic Laws to Improve Haplotype Assembly Using Chaos Game Representation

Sequence data are deposited in the form of unphased genotypes and it is not possible to directly identify the location of a particular allele on a specific parental chromosome or haplotype. This study employed nonlinear time series modeling approaches to analyze the haplotype sequences obtained from the NGS sequencing method. To evaluate the chaotic behavior of haplotypes, we analyzed their whole sequences, as well as several subsequences from distinct haplotypes, in terms of the SNP distribution on their chromosomes. This analysis utilized chaos game representation (CGR) followed by the application of two different scaling methods. It was found that chaotic behavior clearly exists in most haplotype subsequences. For testing the applicability of the proposed model, the present research determined the alleles in gap positions and positions with low coverage by using chromosome subsequences in which 10% of each subsequence's alleles are replaced by gaps. After conversion of the subsequences' CGR into the coordinate series, a Local Projection (LP) method predicted the measure of ambiguous positions in the coordinate series. It was discovered that the average reconstruction rate for all input data is more than 97%, demonstrating that applying this knowledge can effectively improve the reconstruction rate of given haplotypes.